iOS Engineer passionate about building e-commerce and AI-enabled mobile experiences. Strong engineering foundation across Swift/SwiftUI with end-to-end development, clean architecture, and test-driven practices.
Experienced in integrating LLM technology with deterministic rule engines to analyze, interpret, and predict infant sleep patterns. Builds robust MVVM-based iOS apps, processes raw logs into clean data models, creates dynamic SwiftUI chart visualizations, and ensures reliable behavior with comprehensive XCTest coverage.
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Developing a proactive iOS application using SwiftUI and a modular MVVM architecture, designed to ease the cognitive load of new mothers by intelligently managing infant sleep routines. Beyond standard data tracking, I am integrating an on-device AI agent that analyzes historical sleep patterns, wake windows, and daily behaviors. This agent dynamically predicts optimal nap schedules and proactively suggests context-aware routine adjustments directly within the UI, without requiring manual prompts. To support this autonomous feature, I engineered a 3-layer orchestration pipeline (Context Ingestion, Background Reasoning, State Mutation) utilizing dedicated background Actors and Swift Concurrency (async/await). This ensures multi-step LLM reasoning executes seamlessly without blocking the Main Actor. Focused on scalable architecture, thread safety, and preserving structural identity during asynchronous UI updates.
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